PROJECT SUMMARY This is an application for an NIMH Mentored Patient-Oriented Research Career Development Award (K23) entitled Neural Biomarkers of ECT Response in Schizophrenia. In this application, Dr. Miklos Argyelan proposes a comprehensive plan for transitioning into an independent translational researcher focused on understanding the neural mechanisms of treatment response in schizophrenia by integrating functional neuroimaging with neuromodulatory bioelectric treatment approaches. Despite its effectiveness and intensive research, the mechanism of action for ECT remains unknown, and currently no clinical or biological biomarkers exist to predict response. In patients with schizophrenia undergoing a trial of ECT (with bitemporal electrode placement), the proposed study will use resting-state functional MRI, as well as electrical field modeling as applied to structural MRI scans, to examine the neural circuitry underlying clinical response. Patients will undergo MRI scanning at baseline, and after the 8th ECT treatment. Results of this proposal may lead to biomarkers that will optimize treatment algorithms for schizophrenia with higher efficacy. Identifying target mechanisms would not only improve the current deployment of bioelectric approaches as part of a ?precision medicine? approach, but could also lead to the development of novel therapies. This line of research will be conducted under the guidance of mentors who are recognized experts in the biomarker research of schizophrenia (Anil K. Malhotra, M.D.), neuromodulation (Georgios Petrides, M.D., Marom Bikson Ph.D.), neuroimaging of ECT (Chris Abbott, M.D.) and analyzing high dimensional datasets (Jing Sui, PhD.). Concurrently, Dr Argyelan will engage in a comprehensive training program which is fully integrated with the research study in the proposal. The training plan contains three domains with corresponding goals: (1) to gain further expertise in designing and conducting clinical trials in schizophrenia, (2) to expand my knowledge in machine learning algorithms, and (3) to learn more about electrical field (EF) modeling techniques. The combination of training in clinical trials, machine learning and neuromodulation will support the planned research to provide the framework to explore and validate biomarkers of disease and treatment response. The culmination of these training activities, combined with the planned research aims under this award, will prepare Dr. Argyelan to develop into an independent translational researcher and to submit a planned R01 in personalized neuromodulation.